The products marked with a * are currently on sale
Who (department)
Product development and management (includes actuarial dpt)
Why (benefits)
Determine forward cost
Create underwriting structure
Develop underwriting standards
How
GLM model
Who (department)
Underwriting
Commercial
Reinsurances
Why (benefits)
Adjust price
Access underwriting results
Win-back customers
Market DD
Heat of the market
How
Web Crawlers
Brokers network
Experimental design
Who (department)
Underwriting
Why (benefits)
Maximize revenue with the client and competition perspectives
Win-back customers
Commercial DD
Marketing plans
How
WtP with experimental design
Market price
Tariff analysis
Who (department)
Underwriting
Why (benefits)
Understand the product life cycle as applied to insurance
Client communication management
How
Estimate/quote (thought IT automated system activities)
Who (department)
Sales rep
Why (benefits)
Determine the best offer to difficult to understand segments (p.ex. SME)
Over promised control
Commercial efficient
How
Brute force with broker&user experience
Artificial intelligence
Who (department)
Portfolio management/Underwriting
Why (benefits)
Assess underwriting results
Develop a view of a portfolio as a whole, rather than a case-specific perspective
How
Brute force with broker&user experience
Artificial intelligence
Who (department)
Portfolio management/Underwriting
Why (benefits)
Boost underwriting results
Develop a view of a portfolio as a whole, rather than a case-specific perspective
How
Identify non-performing segments within a portfolio
Build statistical strategies to refocus an ailing insurance portfolio
Who (department)
Actuarial
Why (benefits)
Determine past cost
Predict results
Present a true and fair view to stakeholders
How
Establish reserving processes and methodologies
Integrate data and results with claims and underwriting
Who (department)
Claims
Why (benefits)
Fraud detection with a staistical score
How
Improve overcharge detection and optimize sample selection with our powerful supplier screening tool
*
Our Main Approach Is Based In the Benford's Law
A statistical law – Benford’s Law (BL) – states that in many naturally occurring collections of numbers, the leading significant digit is likely to be small:
Usually, the digit 1 appears as the leading significant digit about 30% of the time, while 9 appears as the leading significant digit less than 5% of the time.
If the digits were distributed uniformly, they would each occur about 11.1% of the time. BL also makes predictions about the distribution of second digits, third digits, digit combinations, and so on.
We apply this rule to the insurance claims dataset to find strange digit patterns and narrow the list of possible anomalous items, making the entire audit process more manageable. We also pursue other digit patterns besides the ones in BL.
Sign up to hear from us about specials, sales, and events.